This invention relates to expert systems, and more particularly relates to strategy based systems which quiescently and continuously emulate the operation of business environments and the like on the basis of interrelated cumulative information stored in and accessed from an integrated "knowledge" database. This invention also relates to certain processes which allow stategy based systems to effectively and accurately emulate such real world business environments and the like.
As is well known by those skilled in the art, expert systems have been developed to apply inductive reasoning about problems in specific businesses and to generate solutions and explanations, based upon pertinent facts, heuristic rules, and an accumulation of observations, i.e., a knowledge base. Such prior art systems purport to encode the expertise of one or a plurality of human specialists to address typically a narrowly defined set of problems. Examples of such special purpose systems include the Prospector System intended for mineral exploration applications; Internist--1 intended for medical diagnosis applications; and GE's Delta/CATS--1 intended for locomotive maintenance applications. Thus, the Internist seeks to produce useful diagnostic output for medical professionals. Similarly, Delta/CATS seeks to generate a locomotive maintenance schedule to promote the purposes of users charged with the responsibility for the longevity and proper operation of locomotive engines.
As appreciated by those knowledgeable in the art, expert systems were among the first commercial applications of artificial intelligence technology. The early success of such expert systems led to the growth of a secondary market for expert system shells: tools that allow software developers to build knowledge bases and create expert systems from these tools for specific applications. It has become clear in the art, however, that development of expert systems even using shell-tools and the like have required considerable expertise on the part of software developers in order to produce a practical, working model.
Furthermore, it is typically the case that existing expert systems are virtually marketed exclusively to the artificial intelligence community due to the inherent complexity of these systems. These existing expert systems, viewed as development tools for complex applications--such as medical or mineral exploration and prospecting--rarely are marketed to end users. The customized expert systems which have been ultimately delivered to end users have proven to be costly because of high customization costs coupled with high costs for the development tools themselves.
While expert systems have also been developed for simpler applications, such rudimentary systems have merely conducted question and answer sessions with users. Based upon users' responses, these simple expert systems attempt to emulate human decision making and to provide suggestions for humans' current or future actions. As will be appreciated by those skilled in the art, the usefulness of these systems is limited to a narrow application primarily because of an inherently narrow field of expertise. Accordingly, these modest expert systems have essentially no value outside of their intended scope; such attempted application generally produces combinatorially explosive, and hence uncontrolled, unpredictable behavior. Unknown in the art are closed systems which can function effectively in a diversity of business environments, provided that operating and behavioral rules may be defined.
Regardless of the complexity and scope of expert systems known in the prior art, there is a clear reliance upon human intervention for all strategic instructions regarding what current or future actions should be taken by users. The panoply of anticipated actions and sequences of such actions are "programmed" by humans. As should be evident to those skilled in the art, such programming requires comprehension of the actions that constitute the processes that characterize a particular application and, of course, also requires comprehension of the interrelationships between these processes. Since all business activities inherently have repetitive cyclical processes, so long as human experts conversant with these processes and the constituent activities, and the interrelationships between these activities and processes, can completely define and effectively communicate them to computers, expert systems should theoretically be possible to implement But a disadvantage and limitation of expert systems known in the art has been the necessity for active and significant human intervention and decision-making feedback, even to the extent of requiring human interpretation of output.
On the other hand, a strategy based system allows for human decision-making and then relies upon a database of accumulated information or "knowledge" to ascertain what subsequent actions should be taken. The precursor of such a strategy based system has been expert systems which operate either automatically or semi-automatically using computer technology. As should be known to those skilled in the art, automatic processing has been used for several years, but hardware operation limitations and related computer programming limitations have prevented general application of expert systems due to such inherent problems as combinatorial explosion and the like. Thus, there appears to be no method or means in the art which provides a strategy based expert system which has the capability to routinely process the current and future actions of a business environment with minimal human intervention.
As should be appreciated by those skilled in the art, having expert systems with the ability to quantify what has otherwise been unquantifiable would provide computerized solutions (via "smart systems") for a plethora of business environments which support decision-making in a manner superior to traditional systems. Operating on conventional computers, such a smart system would be able to perform not only conventional number-crunching functions, but also to process non-numeric data such as actions which, indeed, permeate business activity.
As understood by those skilled in the art, expert systems strive to emulate the decision-making processes of humans who are aware of the various available choices to be made responding to a diversity of situations, or who require addition of other choices because of changing conditions. These decision-making processes are neither simple nor practicable by the addition of one more "rule" which may lead to a combinatorial explosion. For example, suppose that a particular process consists of three steps, with each step affording ten choices. As will be appreciated by those skilled in the art, adding one more choice at the first step, causes there to be 100 more possible outcomes of the process. Having the requisite decision-making performed by a human for what is typically a fundamental, recurring business function, vis a vis a more complex function like diagnosing ailments, and merely relying on a knowledge base to perform mundane but time-consuming ensuing activities, differentiates a strategy based system from an expert system.
The kind of choices that must be made by smart systems may be considered to be predefined "actions." While an action may take many forms, it is convenient to assume that a common action is an event. The event should, of course, relate to and be appropriate for a particular business scenario and the like. Thus, a series or sequence of events constitute the lowest common denominator which defines the activities of a particular business or organizational environment.
For instance, medical billing comprises a repetitive procedure that follows predetermined paths depending from the outcomes of a plurality of previous actions. At each decision point or cross-road in this procedure, a human must decide upon a course of action. Various mundane actions must be performed for humans to progress from one step to the next step in such a repetitive procedure. For example, secretaries are required to write letters that have probably been written many times before. As another example, workers make identical mistakes over and over again. As still other examples, lawyers inadvertently forget about case schedules; personnel change jobs and replacements must be trained; humans generally forget details. As should be appreciated by those skilled in the art, the more details that constitute a business process, the more likely that details will be forgotten; indeed, detailed processes are likely not to be performed at all because of typical compelling time and manpower constraints. Thus, virtually every commercial environment has a finite collection of processes which seem to never get performed.
Thus, it would be advantageous to have a closed computer system in which actions are sequentially processed on the basis of virtually nothing being designated as being insignificant. In such a smart system, inherently no data would be ignored and nothing would be forgotten. Such a system would consider all accumulated and current information throughout its processing, i.e., such a system would inherently have no data discrimination, but all accumulated knowledge would be utilized as a Gestalt to orchestrate activities and processes. If and whenever a rule of operation and the like were broken, a smart system would take appropriate action. Indeed, if deemed to be necessary to the business climate and the like, a smart system could even issue instructions to humans. Similarly, mundane activities would be performed flawlessly and with absolute attention to detail. An automated, computerized smart system, of course, does not become bored; it can be a strict disciplinarian by exception-reporting, wherein certain activities must be performed; or such a system can assume an advisory role providing for future activities and events.
The continuous operation of such a smart system would not require accurate quantitative data since numeric information is not the cornerstone thereof. Another important aspect of such a smart system would be to afford the capability to regularly track and monitor pending activities and events: each scheduled activity and each planned event should be thoroughly understood to the knowledge base relative to the operation of a business. To properly emulate a real world business environment, it would be preferable for such a knowledge base to capture the relationship of pending activities and events with all other attributes of the business, including employees, customers, and any and all other pertinent entities. Of course, in addition, once such pending activities and events are accomplished or otherwise resolved, a smart system should duly record the effectuated activities and events for historical purposes.
While there are scheduling systems and the like known in the art that display pending or impending events that are due to occur, and while there are also systems known that record events that have already occurred, there appears to be no strategy based system that allows users to define the plurality of pending and impending activities which are scheduled to occur by modeling individual business functions that constitute internal organizational work flows and predictable, consequent follow up activities. Furthermore, such prior art scheduling systems require that all pending activities be performed by humans. Smart systems, on the other hand, should not only allow scheduled activities to be performed automatically by computer, but also should, indeed, allow for automatic scheduling of predictable, consequent follow up activities.
Accordingly, these limitations and disadvantages of the prior art are overcome with the present invention, and improved means and techniques are provided which are useful for adaptively applying a dynamic user-defined knowledge base to routinely process the current and future activities and events to emulate a plethora of business environments with either minimal or no human intervention, and with the capability to deliver a user-definable computerized smart system.